






import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				a_		:    Tuple   							=     'platform'
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class 		_snake_case	:
			_lowercase		:     int        =				PegasusConfig
			_lowercase		:     str        =				{}
			_lowercase		:     Union[str, Any]        =				'''gelu'''






			def __init__(							self		,       a		,       a=13		,       a=7		,       a=True		,       a=False		,       a=99		,       a=32		,       a=5		,       a=4		,       a=37		,       a=0.1		,       a=0.1		,       a=20		,       a=2		,       a=1		,       a=0		,       )   ->							int:
						SCREAMING_SNAKE_CASE							=			parent
						SCREAMING_SNAKE_CASE							=			batch_size
						SCREAMING_SNAKE_CASE							=			seq_length
						SCREAMING_SNAKE_CASE							=			is_training
						SCREAMING_SNAKE_CASE							=			use_labels
						SCREAMING_SNAKE_CASE							=			vocab_size
						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			intermediate_size

						SCREAMING_SNAKE_CASE							=			hidden_dropout_prob
						SCREAMING_SNAKE_CASE							=			attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE							=			max_position_embeddings
						SCREAMING_SNAKE_CASE							=			eos_token_id
						SCREAMING_SNAKE_CASE							=			pad_token_id
						SCREAMING_SNAKE_CASE							=			bos_token_id






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length - 1]		,       self.vocab_size).clip(3		,       self.vocab_size)
						SCREAMING_SNAKE_CASE							=			np.expand_dims(np.array([self.eos_token_id] * self.batch_size)		,       1)
						SCREAMING_SNAKE_CASE							=			np.concatenate([input_ids, eos_tensor]		,       axis=1)

						SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size)

						SCREAMING_SNAKE_CASE							=			self.config_cls(
						    vocab_size=self.vocab_size		,       d_model=self.hidden_size		,       encoder_layers=self.num_hidden_layers		,       decoder_layers=self.num_hidden_layers		,       encoder_attention_heads=self.num_attention_heads		,       decoder_attention_heads=self.num_attention_heads		,       encoder_ffn_dim=self.intermediate_size		,       decoder_ffn_dim=self.intermediate_size		,       dropout=self.hidden_dropout_prob		,       attention_dropout=self.attention_probs_dropout_prob		,       max_position_embeddings=self.max_position_embeddings		,       eos_token_ids=[2]		,       bos_token_id=self.bos_token_id		,       pad_token_id=self.pad_token_id		,       decoder_start_token_id=self.pad_token_id		,       **self.config_updates		,       )
						SCREAMING_SNAKE_CASE							=			prepare_pegasus_inputs_dict(a		,       a		,       a)
						return config, inputs_dict






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a)   ->							str:
						SCREAMING_SNAKE_CASE							=			20
						SCREAMING_SNAKE_CASE							=			model_class_name(a)

						SCREAMING_SNAKE_CASE							=			model.encode(inputs_dict['input_ids'])

						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			(
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						SCREAMING_SNAKE_CASE							=			model.init_cache(decoder_input_ids.shape[0]		,       a		,       a)
						SCREAMING_SNAKE_CASE							=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,       dtype='i4')

						SCREAMING_SNAKE_CASE							=			jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]		,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,       )
						SCREAMING_SNAKE_CASE							=			model.decode(
						    decoder_input_ids[:, :-1]		,       a		,       decoder_attention_mask=a		,       past_key_values=a		,       decoder_position_ids=a		,       )

						SCREAMING_SNAKE_CASE							=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,       dtype='i4')
						SCREAMING_SNAKE_CASE							=			model.decode(
						    decoder_input_ids[:, -1:]		,       a		,       decoder_attention_mask=a		,       past_key_values=outputs_cache.past_key_values		,       decoder_position_ids=a		,       )

						SCREAMING_SNAKE_CASE							=			model.decode(a		,       a)

						SCREAMING_SNAKE_CASE							=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
						self.parent.assertTrue(diff < 1E-3		,       msg=f'''Max diff is {diff}''')







			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a)   ->							Any:
						SCREAMING_SNAKE_CASE							=			20
						SCREAMING_SNAKE_CASE							=			model_class_name(a)

						SCREAMING_SNAKE_CASE							=			model.encode(inputs_dict['input_ids'])

						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			(
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						SCREAMING_SNAKE_CASE							=			jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
						    ]		,       axis=-1		,       )

						SCREAMING_SNAKE_CASE							=			model.init_cache(decoder_input_ids.shape[0]		,       a		,       a)
						SCREAMING_SNAKE_CASE							=			jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]		,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,       )

						SCREAMING_SNAKE_CASE							=			model.decode(
						    decoder_input_ids[:, :-1]		,       a		,       decoder_attention_mask=a		,       past_key_values=a		,       decoder_position_ids=a		,       )
						SCREAMING_SNAKE_CASE							=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,       dtype='i4')
						SCREAMING_SNAKE_CASE							=			model.decode(
						    decoder_input_ids[:, -1:]		,       a		,       past_key_values=outputs_cache.past_key_values		,       decoder_attention_mask=a		,       decoder_position_ids=a		,       )

						SCREAMING_SNAKE_CASE							=			model.decode(a		,       a		,       decoder_attention_mask=a)

						SCREAMING_SNAKE_CASE							=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
						self.parent.assertTrue(diff < 1E-3		,       msg=f'''Max diff is {diff}''')






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase=None				,							_UpperCAmelCase=None				,							):
			if attention_mask is None:
						SCREAMING_SNAKE_CASE							=			np.not_equal(_UpperCAmelCase				,							config.pad_token_id).astype(np.inta)
			if decoder_attention_mask is None:
						SCREAMING_SNAKE_CASE							=			np.concatenate(
						    [
						        np.ones(decoder_input_ids[:, :1].shape				,							dtype=np.inta),
						        np.not_equal(decoder_input_ids[:, 1:]				,							config.pad_token_id).astype(np.inta),
						    ]				,							axis=-1				,							)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}







@require_flax
class 		_snake_case	(	A__ ,     unittest.TestCase       ):
			_lowercase		:     List[Any]        =				(
			    (
			        FlaxPegasusForConditionalGeneration,
			        FlaxPegasusModel,
			    )
			    if is_flax_available()
			    else ()
			)
			_lowercase		:     Any        =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
			_lowercase		:     str        =				True
			_lowercase		:     List[str]        =				False
			_lowercase		:     Any        =				False
			_lowercase		:     Dict        =				False






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			FlaxPegasusModelTester(self)
						SCREAMING_SNAKE_CASE							=			ConfigTester(self		,       config_class=a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						self.config_tester.run_common_tests()






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(a		,       a		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(a		,       a		,       a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
												SCREAMING_SNAKE_CASE							=			self._prepare_for_class(a		,       a)
												SCREAMING_SNAKE_CASE							=			model_class(a)

												@jax.jit
												def encode_jitted(a		,       a=None		,       **a):
															return model.encode(input_ids=a		,       attention_mask=a)

												with self.subTest('JIT Enabled'):
															SCREAMING_SNAKE_CASE							=			encode_jitted(**a).to_tuple()

												with self.subTest('JIT Disabled'):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE							=			encode_jitted(**a).to_tuple()

												self.assertEqual(len(a)		,       len(a))
												for jitted_output, output in zip(a		,       a):
															self.assertEqual(jitted_output.shape		,       output.shape)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
												SCREAMING_SNAKE_CASE							=			model_class(a)
												SCREAMING_SNAKE_CASE							=			model.encode(inputs_dict['input_ids']		,       inputs_dict['attention_mask'])

												SCREAMING_SNAKE_CASE							=			{
												    'decoder_input_ids': inputs_dict['decoder_input_ids'],
												    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
												    'encoder_outputs': encoder_outputs,
												}

												@jax.jit
												def decode_jitted(a		,       a		,       a):
															return model.decode(
															    decoder_input_ids=a		,       decoder_attention_mask=a		,       encoder_outputs=a		,       )

												with self.subTest('JIT Enabled'):
															SCREAMING_SNAKE_CASE							=			decode_jitted(**a).to_tuple()

												with self.subTest('JIT Disabled'):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE							=			decode_jitted(**a).to_tuple()

												self.assertEqual(len(a)		,       len(a))
												for jitted_output, output in zip(a		,       a):
															self.assertEqual(jitted_output.shape		,       output.shape)






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE							=			model_class_name.from_pretrained('google/pegasus-large'		,       from_pt=a)
									SCREAMING_SNAKE_CASE							=			np.ones((1, 1))
									SCREAMING_SNAKE_CASE							=			model(a)
									self.assertIsNotNone(a)







			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum')
						SCREAMING_SNAKE_CASE							=			PegasusTokenizer.from_pretrained('google/pegasus-xsum')

						SCREAMING_SNAKE_CASE							=			[
						    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
						    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
						]

						SCREAMING_SNAKE_CASE							=			[
						    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
						    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
						]

						SCREAMING_SNAKE_CASE							=			tokenizer(a		,       return_tensors='np'		,       truncation=a		,       max_length=512		,       padding=a)
						SCREAMING_SNAKE_CASE							=			model.generate(**a		,       num_beams=2).sequences
						SCREAMING_SNAKE_CASE							=			tokenizer.batch_decode(a		,       skip_special_tokens=a)
						assert tgt_text == decoded










"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        __magic_name__				(			UpperCAmelCase__		,   unittest.TestCase							):






				'''simple docstring'''
				__UpperCamelCase         =		DiTPipeline
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				__UpperCamelCase         =		PipelineTesterMixin.required_optional_params - {
				    "latents",
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				__UpperCamelCase         =		False





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							torch.manual_seed(0					)
							lowerCamelCase      =   TransformeraDModel(
							    sample_size=16						,					num_layers=2						,					patch_size=4						,					attention_head_dim=8						,					num_attention_heads=2						,					in_channels=4						,					out_channels=8						,					attention_bias=_a						,					activation_fn="""gelu-approximate"""						,					num_embeds_ada_norm=1_000						,					norm_type="""ada_norm_zero"""						,					norm_elementwise_affine=_a						,					)
							lowerCamelCase      =   AutoencoderKL()
							lowerCamelCase      =   DDIMScheduler()
							lowerCamelCase      =   {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
							return components





				def   _lowerCAmelCase							(			self						,					_a						,					_a=0					):





							"""simple docstring"""
							if str(_a					).startswith("""mps"""					):
										lowerCamelCase      =   torch.manual_seed(_a					)
							else:
										lowerCamelCase      =   torch.Generator(device=_a					).manual_seed(_a					)
							lowerCamelCase      =   {
							    """class_labels""": [1],
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}
							return inputs





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   """cpu"""

							lowerCamelCase      =   self.get_dummy_components()
							lowerCamelCase      =   self.pipeline_class(**_a					)
							pipe.to(_a					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs(_a					)
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							self.assertEqual(image.shape						,					(1, 16, 16, 3)					)
							lowerCamelCase      =   np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]					)
							lowerCamelCase      =   np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(_a						,					1e-3					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_inference_batch_single_identical(relax_max_difference=_a						,					expected_max_diff=1e-3					)





				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()						,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,					)
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






@require_torch_gpu
@slow
class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   torch.manual_seed(0					)

							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella""", """white shark""", """white wolf"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=40						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'					)
										assert np.abs((expected_image - image).max()					) < 1e-2





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""					)
							lowerCamelCase      =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   torch.manual_seed(0					)
							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=25						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    f'/dit/{word}_512.npy'					)

										assert np.abs((expected_image - image).max()					) < 1e-1



